Tag: ML Model
Introducing automatic training for solutions in Amazon Personalize | Amazon Web Services
Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure...
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Nielsen Sports sees 75% cost reduction in video analysis with Amazon SageMaker multi-model endpoints | Amazon Web Services
This is a guest post co-written with Tamir Rubinsky and Aviad Aranias from Nielsen Sports.
Nielsen Sports...
Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker | Amazon Web Services
This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML.
Many organizations are...
Enable data sharing through federated learning: A policy approach for chief digital officers | Amazon Web Services
This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, Inc.
...
Moderate audio and text chats using AWS AI services and LLMs | Amazon Web Services
Online gaming and social communities offer voice and text chat functionality for their users to communicate. Although voice and text chat often support friendly...
Unlock personalized experiences powered by AI using Amazon Personalize and Amazon OpenSearch Service | Amazon Web Services
OpenSearch is a scalable, flexible, and extensible open source software suite for search, analytics, security monitoring, and observability applications, licensed under the Apache 2.0...
Automate Amazon SageMaker Pipelines DAG creation | Amazon Web Services
Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we...
Detect anomalies in manufacturing data using Amazon SageMaker Canvas | Amazon Web Services
With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable...
Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access | Amazon Web Services
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs...
How Booking.com modernized its ML experimentation framework with Amazon SageMaker | Amazon Web Services
This post is co-written with Kostia Kofman and Jenny Tokar from Booking.com.
As a global leader in...
Automate mortgage document fraud detection using an ML model and business-defined rules with Amazon Fraud Detector: Part 3 | Amazon Web Services
In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at...
Introducing Allora: A Self-Improving Decentralized AI Network
Polychain, Framework, Blockchain Capital, and CoinFund backed, Allora is poised to transform crypto through the power of decentralized AI
NEW YORK–(BUSINESS WIRE)–Upshot, a leader in...
Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning | Amazon Web Services
In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model...